Poisson Recursive Partitioning and Regression Trees with Exposures

This function is a wrapper around rpart::rpart() for Poisson regression trees using weighted exposures (observation times).

Usage

rpart_exposure(
  formula,
  data,
  exposure_col = "exposure",
  weights = NULL,
  control,
  cost,
  shrink = 1,
  ...
)

Arguments

formula

A model formula that contains a single response variable on the left-hand side.

data

Optional. A data frame containing variables used in the model.

exposure_col

Character string. The name of a column in data containing exposures.

weights

Optional weights to use in the fitting process.

control

A list of hyperparameters. See rpart::rpart.control().

cost

A vector of non-negative costs for each variable in the model.

shrink

Optional parameter for the splitting function. Coefficient of variation of the prior distribution.

...

Alternative input for arguments passed to rpart::rpart.control().

Value

An rpart model

Details

Outside of the tidymodels ecosystem, rpart_exposure() has no advantages over rpart::rpart() since that function allows for exposures to be specified in the formula interface by passing cbind(exposure, y) as a response variable.

Within tidymodels, rpart_exposure() provides an advantage because it will ensure that exposures are included in the data whenever resamples are created.

The formula, data, weights, control, and cost arguments have the same meanings as rpart::rpart(). shrink is passed to rpart::rpart()'s parms argument via a named list. See that function's documentation for full details.

See also

rpart::rpart()

Examples

rpart_exposure(deaths ~ age_group + gender, us_deaths,
               exposure_col = "population")
#> n= 140 
#> 
#> node), split, n, deviance, yval
#>       * denotes terminal node
#> 
#>  1) root 140 51701770.0 0.012433090  
#>    2) age_group=25-34,35-44,45-54,55-64,65-74 100 10090330.0 0.005996979  
#>      4) age_group=25-34,35-44,45-54 60  1037681.0 0.002401215  
#>        8) age_group=25-34,35-44 40   219942.2 0.001550297 *
#>        9) age_group=45-54 20   102550.3 0.004096230 *
#>      5) age_group=55-64,65-74 40  1598645.0 0.012700650  
#>       10) age_group=55-64 20   246107.6 0.008899093 *
#>       11) age_group=65-74 20   237356.1 0.018263680 *
#>    3) age_group=75-84,85+ 40  4697172.0 0.073516860  
#>      6) age_group=75-84 20   170261.7 0.045847100 *
#>      7) age_group=85+ 20    45488.2 0.137105000 *